Abstract
This paper proposes a novel adaptive dynamic programming (ADP) approach to address the optimal consensus control problem for discrete-time multiagent systems (MASs). Compared with the traditional optimal control algorithms for MASs, the proposed algorithm is designed on the basis of the event-triggered scheme which can save the communication and computation resources. First, the consensus tracking problem is transferred into the input-state stable (ISS) problem. Based on this, the event-triggered condition for each agent is designed and the event-triggered ADP is presented. Second, neural networks are introduced to simplify the application of the proposed algorithm. Third, the stability analysis of the MASs under the event-triggered conditions is provided and the estimate errors of the neural networks’ weights are also proved to be ultimately uniformly bounded. Finally, the simulation results demonstrate the effectiveness of the event-triggered ADP consensus control method.
Highlights
Because of the wide applications in the control field [1–6], the consensus control of multiagent systems (MASs) gained more and more attentions
In [26], an Event-triggered control (ETC) method based on adaptive dynamic programming (ADP) is developed for continuous-time MASs. e authors considered the unknown internal states factor in the event-triggered optimal control for continuous-time MASs in [27]. e multiplayer zero-sum differential games are considered in [28] and an optimal consensus tracking control based on event-triggered is designed to solve this problem
We prove the weight estimate errors for the critic neural networks (NNs) and actor NNs are uniformly bounded during the learning process
Summary
Because of the wide applications in the control field [1–6], the consensus control of MASs gained more and more attentions. E multiplayer zero-sum differential games are considered in [28] and an optimal consensus tracking control based on event-triggered is designed to solve this problem. It is worthy to say, all the results in [26–29] studied the event-triggered optimal control for continuous-time MASs, but there were few works [30, 31] which consider the discrete-time MASs. Motivated by the above discussions, an event-triggered ADP control algorithm is designed to address the optimal consensus tracking problem for discrete-time MASs. e major contributions of this paper are emphasized as follows:. (1) Comparing with the existing event-triggered ADP consensus control methods [27–29], we design the adaptive ET condition for every agent in the MASs. en, the agent samples the data and communicates with the neighbors only when its event-triggered condition is satisfied.
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